{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PP-L价差投机--策略性能分析报告"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 策略基本情况\n",
    "\n",
    "### 策略逻辑：\n",
    "    对价差信号，做择时投机\n",
    "    \n",
    "### 操作周期\n",
    "    tick级别\n",
    "\n",
    "### 进场信号：\n",
    "    价差突变，产生择时信号\n",
    "\n",
    "### 出场信号：\n",
    "    1.固定止盈\n",
    "    2.超时止损\n",
    "    3.固定止损\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 需要完成的回测内容\n",
    "\n",
    "    为了完成完整的分析报告，请完成下面的内容：\n",
    "    1.快速模式和慢速模式的回测\n",
    "    2.参数扫描回测\n",
    "    3.不同品种的回测\n",
    "    4.使用步骤2.获得的稳定参数，在样本时间外回测\n",
    "    5.填写下面的策略基本情况，将会根据回测内容自动生成策略报告\n",
    "    \n",
    "## 生成的分析内容\n",
    "    \n",
    "    1.策略速度要求分析\n",
    "    2.策略参数稳定性分析\n",
    "    3.策略的品种稳定性\n",
    "    4.策略的样本外回测\n",
    "    5.实盘记录 \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 策略的基本参数（需要修改的地方）\n",
    "#############################################################\n",
    "\n",
    "# 策略占用的总资金\n",
    "strategyCap  = 9000\n",
    "\n",
    "# 策略回测使用的时间区间\n",
    "startDate = '20160331'\n",
    "endDate = '20160815'\n",
    "\n",
    "# 快速回测的策略名\n",
    "strategyNameF = 'tl for pp-l f'\n",
    "\n",
    "# 慢速回测的策略名\n",
    "strategyNameS = 'tl for pp-l s'\n",
    "\n",
    "# 参数扫描的策略名\n",
    "strategyName = 'tl for pp-l' \n",
    "\n",
    "# 其他品种上的回测\n",
    "strategyNameL = ['tl for a-m','tl for y-OI']\n",
    "strategyCapL  = [6000,9000]\n",
    "\n",
    "# 样本外回测的策略名\n",
    "strategyNameOS = 'tl for pp-l 0'\n",
    "\n",
    "# 样本外回测使用的时间区间\n",
    "startDateOS = '20160801'\n",
    "endDateOS = '20161130'\n",
    "\n",
    "#############################################################\n",
    "# 导入需要的库\n",
    "from vtFunction import *\n",
    "from ctaBase import *\n",
    "from sklearn.cluster import Birch\n",
    "from sklearn.cluster import KMeans\n",
    "import os\n",
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 打印盈亏的函数\n",
    "#############################################################\n",
    "def plotCapCv(strategyNames,strategyBases,startDate,endDate,title = u''):\n",
    "    plt.close()\n",
    "    rtns = pd.DataFrame()\n",
    "    caps = pd.DataFrame()\n",
    "    # 获取盈亏数据，并计算每日资金\n",
    "    fields = ['name','date','pnl']\n",
    "    for name,base in zip(strategyNames,strategyBases):\n",
    "        datas = loadStrategyData(CAP_DB_NAME,name,startDate,endDate,fields)\n",
    "        datas['pnl']=datas['pnl']/base\n",
    "        rtns = pd.concat([rtns,datas],axis = 0)\n",
    "        datas=datas.set_index('date')\n",
    "        datas['cap']=datas.apply(np.cumsum)['pnl']\n",
    "        datas.reset_index(drop=False,inplace=True)\n",
    "        caps = pd.concat([caps,datas],axis = 0)\n",
    "\n",
    "    # 处理数据并做图\n",
    "    rtn_table = pd.crosstab(rtns['date'],rtns['name'], values = rtns['pnl'], aggfunc = sum)  #  一维表变为二维表\n",
    "    rtn_table.fillna(0, inplace = True)  #  将NaN置换为0\n",
    "    cap_table = pd.crosstab(caps['date'],caps['name'], values = caps['cap'], aggfunc = sum)  #  一维表变为二维表\n",
    "    cap_table.fillna(method='pad', inplace = True)  #  将NaN置换为0\n",
    "    cap_table.fillna(0, inplace = True)  #  将NaN置换为0\n",
    "    cap_table.plot()\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    return cap_table\n",
    "    \n",
    "# 参数扫描分析函数\n",
    "#############################################################\n",
    "def plotParamInfo(strategyName,n_clust=5):\n",
    "\n",
    "    # 读取参数扫描结果\n",
    "\n",
    "    # 参数扫描结果文件名\n",
    "    fileName  = '..\\\\vn.trader\\\\ctaAlgo\\\\opResults\\\\'+strategyName+'.csv'\n",
    "    dictMean = {}\n",
    "    dictStd  = {}\n",
    "\n",
    "    # 读取参数扫描结果\n",
    "    pdData = pd.read_csv(fileName)\n",
    "\n",
    "    # 只对总收益和最大回撤聚类分析，丢弃其他列\n",
    "    dropList  = ['averageLosing','averageWinning','commiPerT','profitLossRatio'\\\n",
    "                ,'splipPerT','totalResult','winPerT','winningRate']\n",
    "    pdData.drop(dropList,axis=1, inplace=True)\n",
    "\n",
    "    # 获得参数列\n",
    "    paramList = pdData.columns.tolist()\n",
    "    paramList.remove('capital')\n",
    "    paramList.remove('maxDrawdown')\n",
    "\n",
    "    # 在对参数列归一化之前，缓存参数的均值和标准差,用于恢复结果\n",
    "    for param in paramList:\n",
    "        dictMean[param] = np.mean(pdData[param])\n",
    "        dictStd[param]  = np.std(pdData[param])\n",
    "\n",
    "    # 对参数列归一化\n",
    "    pdData[paramList] = pdData[paramList].apply(lambda x:(x-np.mean(x)))\n",
    "    pdData[paramList] = pdData[paramList].apply(lambda x:(x/np.std(x)))\n",
    "    pdData=pdData.reindex(columns=['capital','maxDrawdown']+paramList)\n",
    "\n",
    "    # 对资金列归一化,并根据参数数目，提高权重\n",
    "    numParam = len(paramList)\n",
    "    capList = ['capital','maxDrawdown']\n",
    "    pdData[capList] = pdData[capList].apply(lambda x:(x*numParam/strategyCap))\n",
    "\n",
    "    # 生成聚类数据\n",
    "    data = [tuple(x) for x in pdData.values]\n",
    "\n",
    "    # 输出数据集样本\n",
    "    print u'数据集样本'\n",
    "    print pdData.head(10)\n",
    "    print u''\n",
    "\n",
    "    # KMeans聚类，分析\n",
    "\n",
    "    clf      = KMeans(n_clusters=n_clust)\n",
    "    y_pred   = clf.fit_predict(data)\n",
    "    p_center = clf.cluster_centers_ \n",
    "\n",
    "    #输出聚类预测结果\n",
    "    print u'聚类分析结果'\n",
    "    allLabels  = range(0,n_clust)\n",
    "    allNums    = [y_pred.tolist().count(a) for a in allLabels]\n",
    "    allResults = [(p_center[i,:],allNums[i]) for i in xrange(0,len(p_center))]\n",
    "    allResults = sorted(allResults,key=lambda d:d[0][0],reverse=True)\n",
    "    for res in allResults:\n",
    "        print('#'*80)\n",
    "        print(u'总收益：%8.2f \\t最大回撤：%8.2f \\t集群数目：%d' %(res[0][0]/numParam, res[0][1]/numParam, res[1]))\n",
    "        print('*'*80)\n",
    "        formatStr = u'%-10s\\t'*len(paramList)\n",
    "        formatFlt = u'%-8.2f\\t'*len(paramList)\n",
    "        allParam = res[0][2:]\n",
    "        for i in xrange(0,len(allParam)):\n",
    "            allParam[i] = allParam[i]*dictStd[paramList[i]]+dictMean[paramList[i]]\n",
    "        print(formatStr %tuple(paramList))\n",
    "        print(formatFlt %tuple(allParam))\n",
    "        print u''\n",
    "    print u''\n",
    "\n",
    "    # 可视化绘图\n",
    "\n",
    "    # 创建字典，用于缓存不同类标下，资金和回撤数据\n",
    "    labelDataX = {}\n",
    "    labelDataY = {}\n",
    "\n",
    "    # 获取不同类标的数据，并修正资金和回撤提高的权重\n",
    "    i = 0\n",
    "    while i < len(data):\n",
    "        for j in xrange(0,n_clust):\n",
    "            if y_pred[i]==j:\n",
    "                if str(j) in labelDataX:\n",
    "                    labelDataX[str(j)].append(data[i][0]/numParam)\n",
    "                    labelDataY[str(j)].append(data[i][1]/numParam)\n",
    "                else:\n",
    "                    labelDataX[str(j)] = []\n",
    "                    labelDataY[str(j)] = []\n",
    "                    labelDataX[str(j)].append(data[i][0]/numParam)\n",
    "                    labelDataY[str(j)].append(data[i][1]/numParam)\n",
    "        i=i+1\n",
    "\n",
    "    # 用不同的颜色和标记显示聚类结果\n",
    "    colors  = ['or','og','ob','oy','ok','om','oc','ow']\n",
    "    #markers = [\"x\",\"o\",\"*\",\"+\",\"s\",\"p\",\"1\",\"2\"]\n",
    "    markers = [\"s\",\"s\",\"s\",\"s\",\"s\",\"s\",\"s\",\"s\"]\n",
    "    for j in xrange(0,n_clust):\n",
    "        plt.plot(labelDataX[str(j)], labelDataY[str(j)], colors[j], marker=markers[j])\n",
    "    \n",
    "    #标记聚类中心    \n",
    "    plt.plot(p_center[:,0]/numParam,p_center[:,1]/numParam,'w*',markersize=8) \n",
    "\n",
    "    # 绘制标题\n",
    "    plt.title(u'参数扫描聚类结果')\n",
    "    # 绘制x轴和y轴坐标\n",
    "    plt.xlabel(u'总盈利：capital')\n",
    "    plt.ylabel(u'最大回撤：maxDrawdown')\n",
    "    plt.show()\n",
    "    return allResults"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 策略性能分析\n",
    "\n",
    "### 1.策略速度要求分析\n",
    "    \n",
    "    下面的Cell比较了及时撮合和延迟1Tick撮合，策略的表现。\n",
    "    一般来说:\n",
    "        1.大周期策略对速度要求较小\n",
    "        2.如果两种撮合方式的策略表现比较一致，那么策略对速度要求一般\n",
    "        3.如果策略只在及时撮合的情况下表现较好，则表示策略对速度有较高要求，较难实现盈利\n",
    "    下面的代码打印了两种情况下的资金曲线图和收益表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UxNChQ+nYsWOSYzZu3EinTp0YOnQo77//PosWLaJjx45s2LCBpk2bJhwXExND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\n2EQsKWCGb0jQ/V6tHAMRqS8iE4FHgDtE5CoR6RAiw28MASB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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd732b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从数据库获得每日盈亏，并做图\n",
    "strategyNames = [strategyNameF, strategyNameS]\n",
    "strategyBases = [strategyCap,strategyCap]\n",
    "capFS = plotCapCv(strategyNames,strategyBases,startDate,endDate,title = u'速度性能比较')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.策略参数稳定性分析\n",
    "    \n",
    "    通过对参数扫描结果进行聚类分析，来简单评估策略的参数稳定性\n",
    "    一般来说：\n",
    "        1.如果聚类分析结果中，策略表现较优的一类数目较多,则策略具有更好的参数稳定性\n",
    "        2.如果策略表现较优一类的聚类中心仍然有较好的表现，则策略具有更好的参数稳定性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集样本\n",
      "     capital  maxDrawdown          fEMA   wLimit  wLimit1\n",
      "0  -7.988013    -8.060900 -1.500000e+00 -1.46385     -1.5\n",
      "1 -10.068257   -10.068257 -1.000000e+00 -1.46385     -1.5\n",
      "2  -7.189170    -7.200117 -5.000000e-01 -1.46385     -1.5\n",
      "3  -5.673453    -5.803190  1.110223e-15 -1.46385     -1.5\n",
      "4  -4.891713    -5.088560  5.000000e-01 -1.46385     -1.5\n",
      "5  -4.277637    -4.570433  1.000000e+00 -1.46385     -1.5\n",
      "6  -3.950120    -4.216360  1.500000e+00 -1.46385     -1.5\n",
      "7  -6.652330    -6.725217 -1.500000e+00 -1.46385     -1.0\n",
      "8  -8.648767    -9.028597 -1.000000e+00 -1.46385     -1.0\n",
      "9  -5.913097    -6.431123 -5.000000e-01 -1.46385     -1.0\n",
      "\n",
      "聚类分析结果\n",
      "################################################################################\n",
      "总收益：    0.36 \t最大回撤：   -0.12 \t集群数目：103\n",
      "********************************************************************************\n",
      "fEMA      \twLimit    \twLimit1   \t\n",
      "0.34    \t25.92   \t28.83   \t\n",
      "\n",
      "################################################################################\n",
      "总收益：    0.21 \t最大回撤：   -0.07 \t集群数目：84\n",
      "********************************************************************************\n",
      "fEMA      \twLimit    \twLimit1   \t\n",
      "0.43    \t27.20   \t11.85   \t\n",
      "\n",
      "################################################################################\n",
      "总收益：   -0.10 \t最大回撤：   -0.43 \t集群数目：47\n",
      "********************************************************************************\n",
      "fEMA      \twLimit    \twLimit1   \t\n",
      "0.13    \t21.17   \t15.74   \t\n",
      "\n",
      "################################################################################\n",
      "总收益：   -1.12 \t最大回撤：   -1.21 \t集群数目：43\n",
      "********************************************************************************\n",
      "fEMA      \twLimit    \twLimit1   \t\n",
      "0.33    \t11.28   \t21.74   \t\n",
      "\n",
      "################################################################################\n",
      "总收益：   -2.40 \t最大回撤：   -2.46 \t集群数目：17\n",
      "********************************************************************************\n",
      "fEMA      \twLimit    \twLimit1   \t\n",
      "0.10    \t10.59   \t14.12   \t\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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bzPYkFDr7o7u/ETtWleGcduCU6CEiIpHkUE18iGb9erj++sznZBta6a0pU6YB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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd5d9f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "paramInfo = plotParamInfo(strategyName,n_clust=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.策略的品种稳定性\n",
    "     \n",
    "     对比不同品种上策略的表现\n",
    "     如果在不同品种上，策略也能实现盈利，则证明策略本身的稳定性也越好\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3bs2OHTsy3LNy5Uq8vLwyvaZUdg4ehO++gxEjNPFwd6kn2irXuVXyAXQDygIj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd730650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strategyNames = strategyNameL\n",
    "strategyBases = strategyCapL\n",
    "capV = plotCapCv(strategyNames,strategyBases,startDate,endDate,title = u'其他品种回测')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.策略的样本外回测\n",
    "     \n",
    "     使用相同参数，在样本外回测\n",
    "     如果在样本外，策略也能实现盈利，则证明策略本身的稳定性也越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DyXnn+cGZUnodO/ow8thjfrBwWSksjIDvTmnWLPaBZPFi3xpU0k31cimQiIhU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\nBkkQBEEQBIUTBkkQBEEQBIXz//mE/3LsKbATAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3ae4ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strategyNames = [strategyNameOS]\n",
    "strategyBases = [strategyCap]\n",
    "capOS = plotCapCv(strategyNames,strategyBases,startDateOS,endDateOS,title = u'样本外回测')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.性能总结\n",
    "     \n",
    "     总结上面所有的分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tl for pp-l--策略分析报告\n",
      "\n",
      "1.速度性能\n",
      "################################################################################\n",
      "延时撮合模式下收益为：0.494309166667\n",
      "及时撮合模式下收益为：0.496295916667\n",
      "****************************************\n",
      "结论 ：策略对交易速度无要求\n",
      "\n",
      "2.品种稳定性\n",
      "################################################################################\n",
      "name        tl for a-m  tl for y-OI\n",
      "date                               \n",
      "2016-07-18    0.420387     0.435819\n",
      "****************************************\n",
      "结论 ：所有其他品种可以实现盈利\n",
      "\n",
      "3.参数稳定性\n",
      "################################################################################\n",
      "总收益：    0.36 \t最大回撤：   -0.12 \t集群数目：104\n",
      "总收益：    0.22 \t最大回撤：   -0.07 \t集群数目：84\n",
      "总收益：   -0.10 \t最大回撤：   -0.44 \t集群数目：46\n",
      "总收益：   -1.12 \t最大回撤：   -1.21 \t集群数目：43\n",
      "总收益：   -2.40 \t最大回撤：   -2.46 \t集群数目：17\n",
      "****************************************\n",
      "结论 ：策略参数稳定盈利\n",
      "\n",
      "4.样本外稳定性\n",
      "################################################################################\n",
      "name        tl for pp-l 0\n",
      "date                     \n",
      "2016-11-15        0.14398\n",
      "****************************************\n",
      "结论 ：样本外可以实现盈利\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(strategyName+'--策略分析报告')\n",
    "print('')\n",
    "\n",
    "# 策略速度性能总结\n",
    "print(u'1.速度性能')\n",
    "print('#'*80)\n",
    "capF = capFS.values[-1][0]\n",
    "capS = capFS.values[-1][1]\n",
    "print(u'延时撮合模式下收益为：'+str(capS))\n",
    "print(u'及时撮合模式下收益为：'+str(capF)) \n",
    "print('*'*40)\n",
    "if capF > capS*1.2:\n",
    "    print(u'结论 ：策略对交易速度要求高')\n",
    "else:\n",
    "    print(u'结论 ：策略对交易速度无要求')\n",
    "print('')\n",
    "\n",
    "# 策略品种稳定性总结\n",
    "print(u'2.品种稳定性')\n",
    "print('#'*80)\n",
    "capVT = capV.tail(1)\n",
    "print(capVT)\n",
    "nM = np.count_nonzero(capVT<0)\n",
    "nP = np.count_nonzero(capVT>0)\n",
    "print('*'*40)\n",
    "if nP > 0 and nM ==0:\n",
    "    print(u'结论 ：所有其他品种可以实现盈利')\n",
    "elif nP > 0:\n",
    "    print(u'结论 ：部分其他品种可以实现盈利')\n",
    "else:\n",
    "    print(u'结论 ：无其他品种可以实现盈利')\n",
    "print('')\n",
    "\n",
    "# 策略参数稳定性总结\n",
    "print(u'3.参数稳定性')\n",
    "print('#'*80)\n",
    "aNum = [res[1] for res in allResults]\n",
    "aCap = [res[0][0] for res in allResults]\n",
    "for res in allResults:\n",
    "    print(u'总收益：%8.2f \\t最大回撤：%8.2f \\t集群数目：%d' %(res[0][0]/numParam, res[0][1]/numParam, res[1]))    \n",
    "print('*'*40)\n",
    "if aNum[0] == max(aNum) and aCap[0] > 0:\n",
    "    print(u'结论 ：策略参数稳定,且能盈利')\n",
    "elif aNum[0] == max(aNum):\n",
    "    print(u'结论 ：策略参数稳定,但不能实现盈利')\n",
    "else:\n",
    "    print(u'结论 ：策略参数不稳定')\n",
    "print('')\n",
    "    \n",
    "# 策略品种稳定性总结\n",
    "print(u'4.样本外稳定性')\n",
    "print('#'*80)\n",
    "capOS = capOS.tail(1)\n",
    "print(capOS)\n",
    "cap = capOS.values[-1][0]\n",
    "print('*'*40)\n",
    "if cap>0:\n",
    "    print(u'结论 ：样本外可以实现盈利')\n",
    "else:\n",
    "    print(u'结论 ：样本外不可以实现盈利')\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.实盘记录\n",
    "\n",
    "     使用相同参数，实盘运行的表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g+edh7FhfCrqR/teJSBFSC4NIgS1a5IMce/eGvfaKOxoRkdzos45IgT34ILzz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\npFoGBkmSVMvAIEmSahkYJElSrV9R1NbUv2K0QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa481b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 实盘记录结果文件名\n",
    "fileName  = '..\\\\vn.trader\\\\ctaAlgo\\\\actual\\\\'+strategyName+'.csv'\n",
    "# 读取并处理数据\n",
    "pdData = pd.read_csv(fileName)\n",
    "pdData['cap'] = pdData.apply(np.cumsum)['pnl']\n",
    "pdData['datetime'] = pdData['date'].map(lambda X:datetime.strptime(str(X), '%Y%m%d'))\n",
    "pdData = pdData.set_index('datetime')\n",
    "# 画图\n",
    "pdData['cap'].plot()\n",
    "plt.show()"
   ]
  },
  {
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    "### 7.实盘日志\n",
    "\n",
    "     1.2月10日启动实盘，策略4手一开\n",
    "     2.2月13日转为7手一开\n",
    "     3.2月28日转为3手一开\n",
    "     4.3月8日至今为8手一开\n",
    "     5.4月7日发现程序逻辑漏洞，尾盘未能及时平仓，手动平仓造成损失1000元\n",
    "     6.程序运行期间，因为机房维护造成服务器重启，每次重启需重新初始化，失去多次开仓机会"
   ]
  }
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